% The outline of the thesis
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Chapter 1: Introduction


1.1 The problem of communication in e-health
- the need for electronic storage and communication of health information
- EPR (single site record – about storage)
- EHR (multi site record – mostly about communication)
- difference between an EHR and an EHR-S
- interoperability problems
- functional interoperabilty
- semantic interoperability (see SemanticHEALTH)
- standardised communication to improve interoperability


1.2 Standards to improve semantic interoperability 
- two key components in an e-health infrastructure
- EHR information models
- Clinical terminology systems


1.2.1 EHR standards and specifications
- HL7 RIM
- GEHR and openEHR
- ENV12265, ENV13606, EN13606
- two-level models (helps to distinguish from multi-tier models)
- reference model
- Archetypes
- AOM
- ADL
- Templates
- archetype design process
- community-based approach / archetype repository


1.2.2 Standardisation of clinical vocabulary
- terminology
- Classification systems
- ontology
- other features
- Covering large clinical phenomenon
- Development of complex terminologies
- Features and structure
- Logic based(ontology aspect)




1.3 The research agenda
1.3.1 Research question
- The importance of integrating EHRs with clinical terminology systems
- The difficulty of the integration under the new e-health paradigm (two-level model)
- Communication based on clinical concepts (Archetypes, CDA templates etc)
- The difference between the EHR information models and terminology models (Semantic Gap)
- Archetype modellers have little guidance on modelling clinical information to create reusable clinical artefacts i.e archetypes 


(The creation of archetypes are based on modellers' knowledge of clinical concepts and existing requirement of what information is to be exchanged)


- Issues of managing archetypes (including creating, maintaining and retrieving archetypes)




1.3.2 Aim and motivation
- This thesis hypothesises that a study of the integration of  two-level model EHR and terminology improves interoperability
- A ‘Shadow’ approach utilises the semantics (clinical concepts) encapsulated in these archetypes to enhance EHR-Terminology integration. 


Chapter 2 Background of the research


2.1 Exchange of clinical information between systems
- Electronic Health Record(EHR)
- Controlled vocabulary
- Other standards(including messaging standards)


2.2 EHR/EPR and information model for health data
- The archetype approach (mentioning message based standards and HL7)
- openEHR and EN13606
- The information models of these standards
- Archetype modelling (features of archetype modelling language and tools associated)
- Management of archetypes
- Archetype design process
- Archetype terms
- Archetype repository


2.3 Clinical information modelling methodology
- Health related information conceptualisation
- Modelling by abstraction(to emulate paper-based clinical documents)
- Coding systems(to enable computer processing)


2.3.1 Standardisation of clinical vocabulary
- Local codes vs standard codes (archetype terms are local codes)
- Terminologies in health care
- Classification systems
- Standardised vocabularies
- Covering large clinical phenomenon
- Review of dominant standardised terminologies in health care
- SNOMED-CT, LOINC, ICD (+new interesting), ICPC, ICNP, RxNorm, Mesh, NIC,NOC, NANDA, FMA(?),
- UMLS, a meta-thesaurus to combine them.

Use of medical ontologies and Graphs/Networks
- Graph theory
- Semantic similarity
- Distance of concepts
- The lowest common ancestor


2.3.2 SNOMED-CT
- Development of complex terminologies
- Features and structure
- Logic based(ontology aspect)




2.4 Integration of EHRs and terminologies 
Benefits of associating EHRs with terminologies
- Delivering high quality health information
- Improving the communication of clinical data


Embedding standard terminologies in EHRs
- HL7 information model for ConceptDomain
- Coded content in other clinical applications


Embedding standard terminologies in archetypes 
- Mechanism to reference external terminology in archetypes
- Term-binding


Benefit of linking archetypes to terminology
- Impact of clinical terminologies on archetypes (terminology led archetype design)
- Archetype automatic categorisation


Chapter 3: Related work


Mapping techniques
- Automatic SNOMED encoding
- RELMA loinc mapping tool
- UMLS metathesaurus
- MetaMap(UMLS application)
- SNOMED-CT IHTSDO cross map(particular to HL7 classes/attributes mapping)
- TermInfo project


Terminology to ontology mapping
- Use OWL to represent SNOMED expression(R.Alan)
- Semantic Web
- Natural Language Processing, String matching approach
- UMLS semantic network(an upper-level ontology mapped to every concept in UMLS)


Information Retrieval (IR)
- Search algorithms and Vector Space model
- tf-idf weighing scheme

Common Terminology Service (CTS)
 - CTS ->CTS2 (innovations)
- Terminology service
- Value set


Misc mapping techniques
- MoST system
- UMLS cluster association with archetypes
- Archetype alignment(Jesus,Damon)




Chapter 4: The ‘Terminological Shadow’ method


An Introduction to terminological shadows


How to obtain shadows from archetypes
- Context information
- Search with Lucene (a tf-idf implementation)
- Generic algorithm of mapping archetypes to SNOMED
- Evaluation of the generic algorithm(paper1&1.5)




Significance of shadows
- Revelation of archetype repository coverage
- Improves classification and categorisation of archetypes
- Improves navigation of archetypes (when looking for information related to a medical category in a clinical ontology) 


- Identifying overlaps between SNOMED and archetype model
- Comparing SNOMED concept model and AOM
- Guidelines for improved modelling
- Guidelines for improved mapping
- Compare archetypes with their shadows




Chapter 5: Implementation and quantitative analysis


Evaluation of algorithms used in Shadow generation
- The mechanism of the mapping process
- Manual mapping as the gold standard
- The first study on select archetypes with manual bindings
- The second study on the NHS CfH archetype repository with manual bindings
- The performance of the algorithm in both studies
- Results and analysis[b]


Analysis of archetype repository coverage
- Use the Shadow approach to map an archetype repository to SNOMED top level categories
- Report the coverage of first-level categories of SNOMED
- Report the coverage of second-level categories of SNOMED
- Report the most frequent archetype terms
- Interpretation of the results
- Patterns emerged from the results




Implementation of Archetype comparison
- Method of comparison
- Shadows of archetypes are compared
- The lowest common ancestor in SNOMED
- Scoring the similarity between archetypes
- Results
- Variation of archetype comparison and its benefits


Chapter 6: Discussion and conclusion (contribution)
Initiatives of the thesis
- Context and motivation


What is new in the methodology
- Novelty in the thesis


Investigations
- Steps to address the research problem
1. Discover the semantic gap 
2. Investigate the mapping mechanism
3. Investigate mapping algorithms from IR
4. Conceptualise ‘Terminological Shadows’
5. Evaluate Shadows (paper1&1.5)
6. Use Shadows to see coverage of medical concepts in archetypes
7. Use Shadows to compare archetypes to find similarity
9. More usage of Shadows in future work


Analysis of the results
- Discussion of the results
- Major conclusions


Weakness
It is believed that the Shadow approach relies on the similarity of the EHR information model and SNOMED concept model


Chapter 7: Future work and summary of the research


Generalisation of the mapping algorithm
- Extension to include other terminologies
- Extension to include other EHR models
- Archetype context information awareness
- SNOMED model awareness(to improve mapping accuracy)


Terminological shadow to combine EHR and terminology


Conclusion 
[a]yusheng1224:
relevant?
________________
damon.berry:
Definitely - at the very least as an alternative resource for "bindings" for HL7 records
[b]yusheng1224:
intelligent mapping can be added in future work to improve binding process
